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Automatic Classification of Liver Diseases from Ultrasound Images Using GLRLM Texture Features

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 195)

Abstract

Ultrasound imaging is considered to be one of the most cost-effective and non-invasive techniques for conclusive diagnosis in some cases and preliminary diagnosis in others. Automatic liver tissue characterization and classification from ultrasonic scans have been for long, the concern of many researchers, and has been made possible today by the availability of the most powerful and cost effective computing facilities. Automatic diagnosis and classification systems are used both for quick and accurate diagnosis and as a second opinion tool for clarifications. This paper analyzes the effect of various linear, non linear and diffusion filters in improving the quality of the liver ultrasound images before proceeding to the subsequent phases of feature extraction and classification using Gray Level Run Length Matrix Features and Support Vector Machines respectively.

Keywords

Filters Texture Gray Level Run Length Matrix Classification Support Vector Machine 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Information TechnologyAmrita Vishwa VidyapeethamCoimbatoreIndia
  2. 2.Department of Electronics and Communication EngineeringDr. Mahalingam College of Engineering and TechnologyPollachiIndia

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